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Hierarchical emotional episodic memory for social human robot collaboration

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Abstract

For social human robot collaboration, robots need to effectively remember human experiences and manage emotional experiences as well as repetitive experiences. To implement these functions, the hierarchical emotional episodic memory, using deep adaptive resonance theory network, is proposed in this paper. The proposed memory not only learns emotional experiences, but also has the ability to anticipate future emotional situations. Two parameter modulation processes, delayed consolidation and instant update, are provided. These make emotional experiences reinforce faster, remain for longer, and become more stable and sensitive to analogous experiences. Simulation analysis is conducted to verify the proposed memory, and an experiment is carried out in a kitchen environment to demonstrate social human robot collaboration.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (MSIP) (No. 2014R1A2A1A10051551).

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Authors and Affiliations

  1. School of Electrical Engineering, KAIST (Korea Advanced Institute of Science Technology), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea

    Won-Hyong Lee & Jong-Hwan Kim

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  1. Won-Hyong Lee
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  2. Jong-Hwan Kim
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Correspondence to Jong-Hwan Kim.

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This is one of the several papers published in Autonomous Robots comprising the Special Issue on Learning for Human-Robot Collaboration.

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Lee, WH., Kim, JH. Hierarchical emotional episodic memory for social human robot collaboration. Auton Robot 42, 1087–1102 (2018). https://doi.org/10.1007/s10514-017-9679-0

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  • DOI: https://doi.org/10.1007/s10514-017-9679-0

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